In this letter, an effective low bit-rate image restoration method is proposed, in which image denoising and subspace regression learning are combined. The proposed framework has two parts: image main structure estimation by classical NLM denoising and texture component prediction by subspace joint regression learning. The local regression function are learned from denoised patch to original patch in each subspace, where the corresponding compression image patches are employed to generate anchoring points by the dictionary learning approach. Moreover, we extent Extreme Support Vector Regression (ESVR) as multi-variable nonlinear regression to get more robustness results. Experimental results demonstrate the proposed method achieves favorable performance compared with other leading methods.

Recently, multi-issue closed negotiations have attracted attention in multi-agent systems. In particular, multi-time and multilateral negotiation strategies are important topics in multi-issue closed negotiations. In multi-issue closed negotiations, an automated negotiating agent needs to have strategies for estimating an opponent's utility function by learning the opponent's behaviors since the opponent's utility information is not open to others. However, it is difficult to estimate an opponent's utility function for the following reasons: (1) Training datasets for estimating opponents' utility functions cannot be obtained. (2) It is difficult to apply the learned model to different negotiation domains and opponents. In this paper, we propose a novel method of estimating the opponents' utility functions using boosting based on the least-squares method and nonlinear programming. Our proposed method weights each utility function estimated by several existing utility function estimation methods and outputs improved utility function by summing each weighted function. The existing methods using boosting are based on the frequency-based method, which counts the number of values offered, considering the time elapsed when they offered. Our experimental results demonstrate that the accuracy of estimating opponents' utility functions is significantly improved under various conditions compared with the existing utility function estimation methods without boosting.

In this letter, a new semi-blind approach incorporating the bounded nature of communication sources with the distance between the equalizer outputs and the training sequence is proposed. By utilizing the sparsity property of l1-norm cost function, the proposed algorithm can outperform the semi-blind method based on higher-order statistics (HOS) criterion especially for transmitting sources with non-constant modulus. Experimental results demonstrate that the proposed method shows superior performance over the HOS based semi-blind method and the classical training-based method for QPSK and 16QAM sources equalization. While for 64QAM signal inputs, the proposed algorithm exhibits its superiority in low signal-to-noise-ratio (SNR) conditions compared with the training-based method.

Densification of mmWave smallcells overlaid on the conventional macro cell is considered to be an essential technology for enhanced mobile broadband services and future IoT applications requiring high data rate e.g. automated driving in 5G communication networks. Taking into account actual measurement mobile traffic data which reveal dynamicity in both time and space, this paper proposes a joint optimization of user association and smallcell base station (BS)'s ON/OFF status. The target is to improve the system's energy efficiency while guaranteeing user's satisfaction measured through e.g. delay tolerance. Numerical analyses are conducted to show the effectiveness of the proposed algorithm against dynamic traffic variation.

This paper introduces a technique for automatically generating potential training data from sentences in which entity pairs are not apparently presented in a relation extraction. Most previous works on relation extraction by distant supervision ignored cases in which a relationship may be expressed via null-subjects or anaphora. However, natural language text basically has a network structure that is composed of several sentences. If they are closely related, this is not expressed explicitly in the text, which can make relation extraction difficult. This paper describes a new model that augments a paragraph with a “salient entity” that is determined without parsing. The entity can create additional tuple extraction environments as potential subjects in paragraphs. Including the salient entity as part of the sentential input may allow the proposed method to identify relationships that conventional methods cannot identify. This method also has promising potential applicability to languages for which advanced natural language processing tools are lacking.

Spectrum-based fault localization (SFL) is a lightweight approach, which aims at helping debuggers to identity root causes of failures by measuring suspiciousness for each program component being a fault, and generate a hypothetical fault ranking list. Although SFL techniques have been shown to be effective, the fault component in a buggy program cannot always be ranked at the top due to its complex fault triggering models. However, it is extremely difficult to model the complex triggering models for all buggy programs. To solve this issue, we propose two simple fault triggering models (RIPRα and RIPRβ), and a refinement technique to improve fault absolute ranking based on the two fault triggering models, through ruling out some higher ranked components according to its fault triggering model. Intuitively, our approach is effective if a fault component was ranked within top k in the two fault ranking lists outputted by the two fault localization strategies. Experimental results show that our approach can significantly improve the fault absolute ranking in the three cases.

A wideband design of the waveguide short-slot 2-plane coupler with 2×2 input/output ports is designed, fabricated, and evaluated. Using coupling coefficients of complementary propagating modes which are TE11, TE21, and TE30 modes, the flatness of the output amplitudes of 2-plane coupler is improved. The coupler operates from 4.96GHz to 5.27GHz (bandwidth 6.1%) which is wider than the former coupler without considering the complementary propagating mode from 5.04GHz to 5.17GHz (bandwidth 2.5%).

This paper reports a novel 3D topology optimization method based on the finite difference time domain (FDTD) method for a dielectric lens antenna. To obtain an optimal lens with smooth boundary, we apply normalized Gaussian networks (NGnet) to 3D topology optimization. Using the proposed method, the dielectric lens with desired radiation characteristics can be designed. As an example of the optimization using the proposed method, the width of the main beam is minimized assuming spatial symmetry. In the optimization, the lens is assumed to be loaded on the aperture of a waveguide slot antenna and is smaller compared with the wavelength. It is shown that the optimized lens has narrower beamwidth of the main beam than that of the conventional lens.

The present Letter proposes a design procedure for inducing synchronization in delayed-coupled one-dimensional map networks. We assume the practical situation where the connection delay, the detailed information about the network topology, and the number of the maps are unknown in advance. In such a situation, it is difficult to guarantee the stability of synchronization, since the local stability of a synchronized manifold is equivalent to that of a linear time-variant system. A sufficient condition in robust control theory helps us to derive a simple design procedure. The validity of our design procedure is numerically confirmed.

This paper presents a water leakage monitoring system that gathers acoustic data of water pipes using wireless communication technology and identifies the sound of water leakage using machine leaning technology. To collect acoustic data effectively, this system combines three types of data-collection methods: drive-by, walk-by, and static. To design this system, it is important to ascertain the wireless communication distance that can be achieved with sensors installed in a basement. This paper also reports on radio propagation from underground manholes made from reinforced concrete and resin concrete in residential and commercial areas using the 920 MHz band. We reveal that it is possible to design a practical system that uses radio communication from underground sensors.

The Jacobi-Davidson method and the Riccati method for eigenvalue problems are studied. In the methods, one has to solve a nonlinear equation called the correction equation per iteration, and the difference between the methods comes from how to solve the equation. In the Jacobi-Davidson/Riccati method the correction equation is solved with/without linearization. In the literature, avoiding the linearization is known as an improvement to get a better solution of the equation and bring the faster convergence. In fact, the Riccati method showed superior convergence behavior for some problems. Nevertheless the advantage of the Riccati method is still unclear, because the correction equation is solved not exactly but with low accuracy. In this paper, we analyzed the approximate solution of the correction equation and clarified the point that the Riccati method is specialized for computing particular solutions of eigenvalue problems. The result suggests that the two methods should be selectively used depending on target solutions. Our analysis was verified by numerical experiments.

A wind-noise suppressor with SNR based wind-noise detection and speech-wind discrimination is proposed. Wind-noise detection is performed in each frame and frequency based on the power ratio of the noisy speech and an estimated stationary noise. The detection result is modified by speech presence likelihood representing spectral smoothness to eliminate speech components. To suppress wind noise with little speech distortion, spectral gains are made smaller in the frame and the frequency where wind-noise is detected. Subjective evaluation results show that the 5-grade MOS for the proposed wind-noise suppressor reaches 3.4 and is 0.56 higher than that by a conventional noise suppressor with a statistically significant difference.

This paper discusses VDT syndrome from the point of view of the viewing distance between a computer screen and user's eyes. This paper conducts a series of experiments to show an impact of the viewing distance on task performance. In the experiments, two different viewing distances of 50cm and 350cm with the same viewing angle of 30degrees are taken into consideration. The results show that the long viewing distance enables people to manipulate the mouse more slowly, more correctly and more precisely than the short.

Ensemble learning is widely used in the field of sensor network monitoring and target identification. To improve the generalization ability and classification precision of ensemble learning, we first propose an approximate attribute reduction algorithm based on rough sets in this paper. The reduction algorithm uses mutual information to measure attribute importance and introduces a correction coefficient and an approximation parameter. Based on a random sampling strategy, we use the approximate attribute reduction algorithm to implement the multi-modal sample space perturbation. To further reduce the ensemble size and realize a dynamic subset of base classifiers that best matches the test sample, we define a similarity parameter between the test samples and training sample sets that takes the similarity and number of the training samples into consideration. We then propose a k-means clustering-based dynamic ensemble selection algorithm. Simulations show that the multi-modal perturbation method effectively selects important attributes and reduces the influence of noise on the classification results. The classification precision and runtime of experiments demonstrate the effectiveness of the proposed dynamic ensemble selection algorithm.

Reproducing quadruped locomotion from an engineering viewpoint is important not only to control robot locomotion but also to clarify the nonlinear mechanism for switching between locomotion patterns. In this paper, we reproduced a quadruped locomotion pattern, gallop, using a central pattern generator (CPG) hardware network based on the abelian group Z4×Z2, originally proposed by Golubitsky et al. We have already used the network to generate three locomotion patterns, walk, trot, and bound, by controlling the voltage, EMLR, inputted to all CPGs which acts as a signal from the midbrain locomotor region (MLR). In order to generate the gallop and canter patterns, we first analyzed the network symmetry using group theory. Based on the results of the group theory analysis, we desymmetrized the contralateral couplings of the CPG network using a new parameter in addition to EMLR, because, whereas the walk, trot, and bound patterns were able to be generated from the spatio-temporal symmetry of the product group Z4×Z2, the gallop and canter patterns were not. As a result, using a constant element $hat{kappa}$ on Z2, the gallop and canter locomotion patterns were generated by the network on ${f Z}_4+hat{kappa}{f Z}_4$, and actually in this paper, the gallop locomotion pattern was generated on the actual circuit.

The research on inertial sensor based human action detection and recognition (HADR) is a new area in machine learning. We propose a novel time sequence based interval convolutional neutral networks framework for HADR by combining interesting interval proposals generator and interval-based classifier. Experiments demonstrate the good performance of our method.

Both spatial diversity and multihop relaying are considered to be effective methods for mitigating the impact of atmospheric turbulence-induced fading on the performance of free-space optical (FSO) systems. Multihop relaying can significantly reduce the impact of fading by relaying the information over a number of shorter hops. However, it is not feasible or economical to deploy relays in many practical scenarios. Spatial diversity could substantially reduce the fading variance by introducing additional degrees of freedom in the spatial domain. Nevertheless, its superiority is diminished when the fading sub-channels are correlated. In this paper, our aim is to study the fundamental performance limits of spatial diversity suffering from correlated Gamma-Gamma (G-G) fading channels in multihop coherent FSO systems. For the performance analysis, we propose to approximate the sum of correlated G-G random variables (RVs) as a G-G RV, which is then verified by the Kolmogorov-Smirnov (KS) goodness-of-fit statistical test. Performance metrics, including the outage probability and the ergodic capacity, are newly derived in closed-form expressions and thoroughly investigated. Monte-Carlo (M-C) simulations are also performed to validate the analytical results.

This paper proposes methods of finding a ranked list of entities for a given query (e.g. “Kennin-ji”, “Tenryu-ji”, or “Kinkaku-ji” for the query “ancient zen buddhist temples in kyoto”) by leveraging different types of modifiers in the query through identifying corresponding properties (e.g. established date and location for the modifiers “ancient” and “kyoto”, respectively). While most major search engines provide the entity search functionality that returns a list of entities based on users' queries, entities are neither presented for a wide variety of search queries, nor in the order that users expect. To enhance the effectiveness of entity search, we propose two entity ranking methods. Our first proposed method is a Web-based entity ranking that directly finds relevant entities from Web search results returned in response to the query as a whole, and propagates the estimated relevance to the other entities. The second proposed method is a property-based entity ranking that ranks entities based on properties corresponding to modifiers in the query. To this end, we propose a novel property identification method that identifies a set of relevant properties based on a Support Vector Machine (SVM) using our seven criteria that are effective for different types of modifiers. The experimental results showed that our proposed property identification method could predict more relevant properties than using each of the criteria separately. Moreover, we achieved the best performance for returning a ranked list of relevant entities when using the combination of the Web-based and property-based entity ranking methods.

Review rating prediction is an important problem in machine learning and data mining areas and has attracted much attention in recent years. Most existing methods for review rating prediction on Location-Based Social Networks only capture the semantics of texts, but ignore user information (social links, geolocations, etc.), which makes them less personalized and brings down the prediction accuracy. For example, a user's visit to a venue may be influenced by their friends' suggestions or the travel distance to the venue. To address this problem, we develop a review rating prediction framework named TSG by utilizing users' review Text, Social links and the Geolocation information with machine learning techniques. Experimental results demonstrate the effectiveness of the framework.

In a local region of a color image, the color distribution often takes the form of a linear line in the RGB space. This property is called “Color Line” and we propose a denoising method based on this property. When a noise is added on an image, its color distribution spreads from the Color Line. The denoising is achieved by reducing the spread. In conventional methods, Color Line is assumed to be only a single line, but actual distribution takes various shapes such as a single line, two lines, and a plane and so on. In our method, we estimate the distribution in more detail using plane approximation and denoise each patch by reducing the spread depending on the Color Line types. In this way, we can achieve better denoising results than a conventional method.